Anchoring the human olfactory system within a functional gradient

Waymel, Alice; Friedrich, Patrick; Bastian, Pierre Antoine; Forkel, Stephanie J.; Schotten, Michel Thiebaut de

doi:10.1016/j.neuroimage.2020.116863

Cited by 11 publications

(3 citation statements)

References 34 publications

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“…In addition, other cortical features such as myelination (Huntenburg et al 2017 ) or the representation of event length (Baldassano et al 2017 ) share this topological axis (Huntenburg et al 2018 ). In addition, organizational axes uncovered by manifold learning have been linked to evolutionary principles such as the progressive differentiation of cortical layers (Waymel et al 2020 ; Valk et al 20) as proposed by the dual-origin theory (Dart, 1934 ; Pandya et al 2015 ). Therefore, manifold learning techniques provide useful insights into the topology of the human brain, both on a local/regional scale as well as on a global/whole-brain level.…”

Section: Discussionmentioning

confidence: 99%

Is it left or is it right? A classification approach for investigating hemispheric differences in low and high dimensionality

Friedrich¹,

Patil

Mochalski

et al. 2021

Brain Struct Funct

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Hemispheric asymmetries, i.e., differences between the two halves of the brain, have extensively been studied with respect to both structure and function. Commonly employed pairwise comparisons between left and right are suitable for finding differences between the hemispheres, but they come with several caveats when assessing multiple asymmetries. What is more, they are not designed for identifying the characterizing features of each hemisphere. Here, we present a novel data-driven framework—based on machine learning-based classification—for identifying the characterizing features that underlie hemispheric differences. Using voxel-based morphometry data from two different samples (n = 226, n = 216), we separated the hemispheres along the midline and used two different pipelines: First, for investigating global differences, we embedded the hemispheres into a two-dimensional space and applied a classifier to assess if the hemispheres are distinguishable in their low-dimensional representation. Second, to investigate which voxels show systematic hemispheric differences, we employed two classification approaches promoting feature selection in high dimensions. The two hemispheres were accurately classifiable in both their low-dimensional (accuracies: dataset 1 = 0.838; dataset 2 = 0.850) and high-dimensional (accuracies: dataset 1 = 0.966; dataset 2 = 0.959) representations. In low dimensions, classification of the right hemisphere showed higher precision (dataset 1 = 0.862; dataset 2 = 0.894) compared to the left hemisphere (dataset 1 = 0.818; dataset 2 = 0.816). A feature selection algorithm in the high-dimensional analysis identified voxels that most contribute to accurate classification. In addition, the map of contributing voxels showed a better overlap with moderate to highly lateralized voxels, whereas conventional t test with threshold-free cluster enhancement best resembled the LQ map at lower thresholds. Both the low- and high-dimensional classifiers were capable of identifying the hemispheres in subsamples of the datasets, such as males, females, right-handed, or non-right-handed participants. Our study indicates that hemisphere classification is capable of identifying the hemisphere in their low- and high-dimensional representation as well as delineating brain asymmetries. The concept of hemisphere classifiability thus allows a change in perspective, from asking what differs between the hemispheres towards focusing on the features needed to identify the left and right hemispheres. Taking this perspective on hemispheric differences may contribute to our understanding of what makes each hemisphere special.

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Section: Discussionmentioning

confidence: 99%

Is it left or is it right? A classification approach for investigating hemispheric differences in low and high dimensionality

Friedrich¹,

Patil

Mochalski

et al. 2021

Brain Struct Funct

Self Cite

View full text Add to dashboard Cite

show abstract

“…Their central assumption is that the latent connectivity structure of high-dimensional neuroimaging data can be captured in a low-dimensional space. The dimensions of such a space, referred to as connectivity gradients, have been shown to be meaningful ( Glomb et al, 2021 ; Huntenburg et al, 2018 ; Margulies et al., 2016 ; Waymel et al, 2020 ), and were successfully used to study variations across individuals and species, in health and disease ( Brown et al, 2022 ; Caciagli et al, 2022 ; Dong et al, 2021a ; Guell et al, 2018 ; Hong et al, 2019 ; Larivière et al, 2020a ; Li et al, 2021 ; Meng et al, 2021 ; Mulders et al, 2022 ; Nenning et al, 2017 ; Paquola et al, 2019 ; Park et al, 2022 ; Pasquini et al, 2022 ; Samara et al, 2023 ; Xu et al, 2020 ). Most notably, the sensorimotor-association axis (SA-axis), a defining feature of cortical hierarchy ( Hutchinson and Barrett, 2019 ; Sydnor et al, 2021 ), has been consistently identified to explain most of the variance in the human connectome, thus referred to as the principal gradient ( Margulies et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Omnipresence of the sensorimotor-association axis topography in the human connectome

Nenning

Franco

et al. 2023

NeuroImage

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“…The copyright holder for this this version posted November 2, 2023. ; https://doi.org/10.1101/2023.11.01.565213 doi: bioRxiv preprint characterized by higher intracortical myelin content and higher stability (García-Cabezas et al, 2017;Glasser & Van Essen, 2011). Lastly, gradients of microstructural variation running along major axes of organization in the cortex support variation in brain function (Kharabian Masouleh et al, 2020;Vezoli et al, 2021;Waymel et al, 2020). Thus, examining variations in i) microstructural tissue properties, ii) cortical lamination and iii) the microstructural inter-regional organization in-vivo in relation to endocrine plasticity may yield evidence about the basis of macroscale plasticity observed with neuroimaging.…”

Section: Introductionmentioning

confidence: 99%

Relating sex differences in cortical and hippocampal microstructure to sex hormones

Küchenhoff,

Bayrak,

Zsido

et al. 2023

Preprint

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Sex hormone receptors are expressed widely in both neurons and glial cells, which allows them to interact with the brain’s major cell groups via several molecular mechanisms. These mechanisms lead to sex differences in brain structure as well as hormone-induced plasticity in the female brain across the menstrual cycle. Adding to the literature on volumetric changes in cortical structure, here we set out to investigate sex differences in the microstructure of the human cortex in relation to sex hormones. We assessed regional variation in cortical microstructure as a function of sex, hormonal status and sex hormone receptor gene expression distribution based on quantitative intracortical profilingin vivousing the magnetic resonance imaging based T1w/T2w ratio in 992 healthy females and males of the Human Connectome Project young adult sample. We demonstrate that microstructure in isocortex and hippocampus differs regionally between males and females, that this effect varies with hormone levels of females and that implicated brain regions overlap with estrogen receptor and sex steroid synthesis gene expression. Lastly, we show that sex and sex hormone related brain structure variations are most pronounced in areas of low laminar cortical complexity (agranular cortex), which are also predicted to be most plastic based on their cytoarchitectural properties. Together, our data thus are suggestive of sex differences in cortical and hippocampal microstructure, as well as the modulatory function of sex hormones on these measures. Albeit correlative, this study underscores the importance of incorporating sex hormone variables into the investigation of brain structure and plasticity.

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Anchoring the human olfactory system within a functional gradient

Cited by 11 publications

References 34 publications

Is it left or is it right? A classification approach for investigating hemispheric differences in low and high dimensionality

Is it left or is it right? A classification approach for investigating hemispheric differences in low and high dimensionality

Omnipresence of the sensorimotor-association axis topography in the human connectome

Relating sex differences in cortical and hippocampal microstructure to sex hormones

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